IMU-Based Movement Trajectory Heatmaps for Human Activity Recognition

Clicks: 275
ID: 266212
2020
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Abstract
Recent trends in ubiquitous computing have led to a proliferation of studies that focus on human activity recognition (HAR) utilizing inertial sensor data that consist of acceleration, orientation and angular velocity. However, the performances of such approaches are limited by the amount of annotated training data, especially in fields where annotating data is highly time-consuming and requires specialized professionals, such as in healthcare. In image classification, this limitation has been mitigated by powerful oversampling techniques such as data augmentation. Using this technique, this work evaluates to what extent transforming inertial sensor data into movement trajectories and into 2D heatmap images can be advantageous for HAR when data are scarce. A convolutional long short-term memory (ConvLSTM) network that incorporates spatiotemporal correlations was used to classify the heatmap images. Evaluation was carried out on Deep Inertial Poser (DIP), a known dataset composed of inertial sensor data. The results obtained suggest that for datasets with large numbers of subjects, using state-of-the-art methods remains the best alternative. However, a performance advantage was achieved for small datasets, which is usually the case in healthcare. Moreover, movement trajectories provide a visual representation of human activities, which can help researchers to better interpret and analyze motion patterns.
Reference Key
konak2020sensorsimu-based Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Orhan Konak;Pit Wegner;Bert Arnrich;Konak, Orhan;Wegner, Pit;Arnrich, Bert;
Journal sensors
Year 2020
DOI
10.3390/s20247179
URL
https://www.mdpi.com/1424-8220/20/24/7179/htm
Keywords
Machine learning Image processing human activity recognition sensor data

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